{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  对男1000米跑、男引体进行等宽分箱操作，分成3份，并使用饼图绘制百分比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "##  对男1000米跑进行等宽分箱操作并画饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#导入包及数据集\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "male = pd.read_excel(r'.\\体测分数_男生.xls')\n",
    "female = pd.read_excel(r'.\\体测分数_女生.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>男1000米跑等宽分箱</th>\n",
       "      <th>男引体等宽分箱</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>68</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>100</td>\n",
       "      <td>176</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>22.440001</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>40</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>7042</td>\n",
       "      <td>100</td>\n",
       "      <td>177</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>24.260000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>100</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80</td>\n",
       "      <td>252</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "      <td>85</td>\n",
       "      <td>5755</td>\n",
       "      <td>100</td>\n",
       "      <td>181</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>19.840000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>5688</td>\n",
       "      <td>100</td>\n",
       "      <td>172</td>\n",
       "      <td>51.700001</td>\n",
       "      <td>17.480000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI 男1000米跑等宽分箱 男引体等宽分箱  \n",
       "0        0  2785      62  170  72.599998  25.120001           3       1  \n",
       "1       60  3133      68  174  52.700001  17.410000           3       1  \n",
       "2        0  3901      80  169  46.500000  16.280001           3       1  \n",
       "3        0  4946     100  183  79.699997  23.799999           3       1  \n",
       "4       68  3538      74  171  54.700001  18.709999           2       2  \n",
       "..     ...   ...     ...  ...        ...        ...         ...     ...  \n",
       "472      0  4647     100  176  69.500000  22.440001           3       1  \n",
       "473     50  7042     100  177  76.000000  24.260000           3       1  \n",
       "474     85  5755     100  181  65.000000  19.840000           2       2  \n",
       "475     76  5688     100  172  51.700001  17.480000           3       2  \n",
       "476      0     0       0    0   0.000000   0.000000           1       1  \n",
       "\n",
       "[477 rows x 19 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对男1000米跑、男引体进行等宽分箱操作\n",
    "male['男1000米跑等宽分箱'] = pd.cut(male['男1000米跑'],3,labels=[1,2,3])\n",
    "male['男引体等宽分箱'] = pd.cut(male['男引体'],3,labels=[1,2,3])\n",
    "male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "男1000米跑等宽分箱\n",
       "1     21\n",
       "2    195\n",
       "3    261\n",
       "Name: 男1000米跑, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计各分箱数量\n",
    "male_count_1000 = male.groupby(male['男1000米跑等宽分箱'])[['男1000米跑']].count()\n",
    "male_count_h = male.groupby(male['男引体等宽分箱'])[['男引体']].count()\n",
    "male_count_1000[\"男1000米跑\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#设置标签\n",
    "male_first_1000 = male[\"男1000米跑\"].min().round(1)\n",
    "male_fourth_1000 = male[\"男1000米跑\"].max().round(1)\n",
    "male_second_1000 = ((male[\"男1000米跑\"].max()-male[\"男1000米跑\"].min())/3).round(1)\n",
    "male_third_1000 = ((male[\"男1000米跑\"].max()-male[\"男1000米跑\"].min())*2/3).round(1)\n",
    "labels = [str(male_first_1000.round(1))+\"-\"+str(male_second_1000),str(male_second_1000)+\"-\"+str(male_third_1000)\\\n",
    "          ,str(male_third_1000)+\"-\"+str(male_fourth_1000)]\n",
    "#绘制饼图\n",
    "_ = plt.pie(x = male_count_1000[\"男1000米跑\"],\n",
    "           labels = labels,\n",
    "           autopct='%0.1f%%')\n",
    "mpl.rcParams[\"font.sans-serif\"]=[\"SimHei\"]\n",
    "_ = plt.title(\"男1000米跑各时间数量占比\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#设置标签\n",
    "male_first_1000 = (male[\"男引体\"].min()).round(1)\n",
    "male_fourth_1000 = (male[\"男引体\"].max()).round(1)\n",
    "male_second_1000 = ((male[\"男引体\"].max()-male[\"男引体\"].min())/3).round(1)\n",
    "male_third_1000 = ((male[\"男引体\"].max()-male[\"男引体\"].min())*2/3).round(1)\n",
    "labels = [str(male_first_1000.round(1))+\"-\"+str(male_second_1000),str(male_second_1000)+\"-\"+str(male_third_1000)\\\n",
    "          ,str(male_third_1000)+\"-\"+str(male_fourth_1000)]\n",
    "#绘制饼图\n",
    "_ = plt.pie(x = male_count_h[\"男引体\"],\n",
    "           labels = labels,\n",
    "           autopct='%0.1f%%')\n",
    "mpl.rcParams[\"font.sans-serif\"]=[\"SimHei\"]\n",
    "_ = plt.title(\"男引体各范围数量占比\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "##  对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制女800米跑直方图\n",
    "cond = female.loc[female.女800米跑 < 6]['女800米跑']\n",
    "plt.hist(x = cond,\n",
    "        bins = 4)\n",
    "_ = plt.title('女800米跑分布')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制女跳远直方图\n",
    "plt.hist(x = female['女跳远'],\n",
    "        bins = 4)\n",
    "_ = plt.title('女跳远分布')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "##  使用嵌套饼图对比男女体重指数进行比例统计，分为正常、低体重、超重、肥胖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BMI等级</th>\n",
       "      <th>性别</th>\n",
       "      <th>性别等级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>超重</td>\n",
       "      <td>男</td>\n",
       "      <td>超重男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>正常</td>\n",
       "      <td>男</td>\n",
       "      <td>正常男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>低体重</td>\n",
       "      <td>男</td>\n",
       "      <td>低体重男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>超重</td>\n",
       "      <td>男</td>\n",
       "      <td>超重男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>正常</td>\n",
       "      <td>男</td>\n",
       "      <td>正常男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>正常</td>\n",
       "      <td>女</td>\n",
       "      <td>正常女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>正常</td>\n",
       "      <td>女</td>\n",
       "      <td>正常女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>正常</td>\n",
       "      <td>女</td>\n",
       "      <td>正常女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>正常</td>\n",
       "      <td>女</td>\n",
       "      <td>正常女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>正常</td>\n",
       "      <td>女</td>\n",
       "      <td>正常女</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1070 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    BMI等级 性别  性别等级\n",
       "0      超重  男   超重男\n",
       "1      正常  男   正常男\n",
       "2     低体重  男  低体重男\n",
       "3      超重  男   超重男\n",
       "4      正常  男   正常男\n",
       "..    ... ..   ...\n",
       "588    正常  女   正常女\n",
       "589    正常  女   正常女\n",
       "590    正常  女   正常女\n",
       "591    正常  女   正常女\n",
       "592    正常  女   正常女\n",
       "\n",
       "[1070 rows x 3 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分箱\n",
    "male['BMI等级'] = pd.cut(male.BMI,\n",
    "      bins = [0,16.4,23.2,26.3,80],\n",
    "      right = False,\n",
    "      labels = ['低体重','正常','超重','肥胖'])\n",
    "\n",
    "female['BMI等级'] = pd.cut(female.BMI,\n",
    "      bins = [0,16.4,22.7,25.2,80],\n",
    "      right = False,\n",
    "      labels = ['低体重','正常','超重','肥胖'])\n",
    "BMI = pd.concat([male['BMI等级'],female['BMI等级']],axis= 0)\n",
    "sex = pd.concat([male['性别'],female['性别']],axis= 0)\n",
    "BMI1 = pd.concat([BMI,sex],axis= 1)\n",
    "BMI1['性别等级'] = BMI1[['BMI等级','性别']].apply(lambda x: ''.join(x), axis=1) \n",
    "BMI1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#统计各数量\n",
    "count_BMI = BMI1.groupby(BMI1['BMI等级'])[['性别']].count()\n",
    "count_BMI_sex = BMI1.groupby(BMI1['性别等级'])[['性别']].count()\n",
    "plt.figure(figsize=(12,12))\n",
    "_ = plt.pie(count_BMI['性别'],\n",
    "       radius=1,\n",
    "       pctdistance=0.85,\n",
    "       labels =['低体重','正常','超重','肥胖'],\n",
    "       autopct='%0.1f%%',\n",
    "       wedgeprops=dict(linewidth=3,width=0.4,edgecolor='w'))\n",
    "_ = plt.pie(count_BMI_sex['性别'],\n",
    "            autopct='%0.1f%%',\n",
    "           radius = 0.7,\n",
    "           pctdistance=0.7,\n",
    "           wedgeprops=dict(linewidth=3,width=0.7,edgecolor='w'),\n",
    "           labels =['低体重男','低体重女','正常男','正常女','超重男','超重女','肥胖男','肥胖女'] ,\n",
    "           labeldistance=0.8)\n",
    "_ = plot.title(\"男女BMI体重等级占比\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
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}
